CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Deep Learning Driven Hypergraph Representation for Image-Based Emotion Recognition
Huang Yuchi; Lu Hanqing; Huang, Yuchi
Conference Name18th ACM International Conference on Multimodal Interaction
Source Publication国际会议
Conference DateNovember 12–16, 2016
Conference PlaceTokyo, Japan
In this paper, we proposed a bi-stage framework for imagebased
emotion recognition by combining the advantages of
deep convolutional neural networks (D-CNN) and hypergraphs.
To exploit the representational power of D-CNN,
we remodeled its last hidden feature layer as the `attribute'
layer in which each hidden unit produces probabilities on a
speci c semantic attribute. To describe the high-order relationship
among facial images, each face was assigned to
various hyperedges according to the computed probabilities
on di erent D-CNN attributes. In this way, we tackled the
emotion prediction problem by a transductive learning approach,
which tends to assign the same label to faces that
share many incidental hyperedges (attributes), with the constraints
that predicted labels of training samples should be
similar to their ground truth labels. We compared the proposed
approach to state-of-the-art methods and its e ectiveness
was demonstrated by extensive experimentation.
KeywordEmotion Recognition Hypergraph Deep Convolutional Networks
Document Type会议论文
Corresponding AuthorHuang, Yuchi
Recommended Citation
GB/T 7714
Huang Yuchi,Lu Hanqing,Huang, Yuchi. Deep Learning Driven Hypergraph Representation for Image-Based Emotion Recognition[C],2016.
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